Automated testing procedures—particularly those used to characterize complicated electromechanical systems—are often costly and time-consuming. As technology levels increase, the number of variables and complexity associated with such components likewise dramatically increases.
In the case of powertrains and other automotive components, for example, there exist so many inputs and outputs associated with the system that the number of measurements required to properly calibrate even one aspect may take weeks to complete. A modern diesel engine, for example, may have as many as thirteen variables requiring calibration. Systematic and exhaustive measurements of such a system would be prohibitively time-consuming and expensive.
Prior art methods typically address this problem by performing individual tests for each calibration of interest. Design of Experiments (DOE) techniques are used to achieve test efficiency by limiting test conditions to a small set, with the assumption that certain inputs to not interact in their effect on the outputs, and/or that the test outputs follow a presumed mathematical relationship. These prior art testing methodologies may not properly take into account interaction between multiple variables, and therefore do not produce models amenable to optimization—e.g., optimization with respect to fuel economy, performance, quality, etc.
Accordingly, there is a need for improved methods and systems for calibration and testing of complicated electromechanical systems.